基于网络药理学和代谢组学分析滁菊中潜在活性成分及其药理作用机制

doi:10.5246/jcps.2022.06.036

中国药学(英文版) ›› 2022, Vol. 31 ›› Issue (6): 412-428.DOI: 10.5246/jcps.2022.06.036

基于网络药理学和代谢组学分析滁菊中潜在活性成分及其药理作用机制

赵维萍¹, 葛奇², 丁子俊¹, 潘雷枝¹, 谷子晴¹, 刘洋¹^,^*(), 蔡华¹^,^*()

1. 滁州学院生物与食品工程学院, 安徽滁州 239000
2. 江苏大学生命科学学院, 江苏镇江 212013

收稿日期:2022-03-12 修回日期:2022-04-20 接受日期:2022-04-25 出版日期:2022-06-30 发布日期:2022-06-30
通讯作者: 刘洋, 蔡华
作者简介:
+ Tel.: +86-13637049517, E-mail: chzu_liuyang@sina.com
+ Tel.: +86-13855061006, E-mail: zwp_1020@chzu.edu.cn
基金资助:
Key Project of Natural Science Research in Colleges and Universities in Anhui Province (Grant No. KJ2019A0639, KJ2020A0715), Open Foundation of the Key Laboratory of Crop Gene Resources and Germplasm Creation in East China, Ministry of Agriculture (Grant No. ECG2018001), the Key Project of Excellent Young Talents Support Program in Universities of Anhui Province (Grant No. gxyqZD2021133), and Chuzhou Science and Technology Plan Project (Grant No. 2020ZN013).

Network pharmacology and metabolomics-based detection of the potential pharmacological effects of the active components in Chrysanthemum morifolium 'Chuju'

Weiping Zhao¹, Qi Ge², Zijun Ding¹, Leizhi Pan¹, Ziqing Gu¹, Yang Liu¹^,^*(), Hua Cai¹^,^*()

1 College of Biology and Food Engineering, Chuzhou University, Chuzhou 239000, Anhui, China
2 School of Life Sciences, Jiangsu University, Zhenjiang 212013, Jiangsu, China

Received:2022-03-12 Revised:2022-04-20 Accepted:2022-04-25 Online:2022-06-30 Published:2022-06-30
Contact: Yang Liu, Hua Cai

摘要/Abstract

摘要：

"滁菊"是我国著名的传统中药材, 它含有多种有效活性成分, 可以治疗多种疾病, 而且没有毒副作用。为了进一步探究"滁菊"中的有效活性成分及其作用靶点和药理机制, 本研究采用代谢组学与网络药理学相结合的方法, 构建了基于化学、药代动力学和药理学的可视化网络分析模型, 对滁菊的潜在药理机制进行了预测。共鉴定出424个代谢产物, 通过ADME筛选出21个具有潜在药用活性成分的化合物。对活性成分进一步的药理学分析发现, 滁菊中的金合花素、β-谷甾醇通过作用于GSK3B、MAPK14、ADRA1R和NOS2等靶点蛋白来调控糖尿病、阿尔茨海默病、乳腺癌和炎症等疾病。这些结果成功地说明了"滁菊"潜在的有效活性成分具有多种药理作用机制。

关键词: 滁菊, 代谢组学, 网络药理学, 抗癌, 抗糖尿病

Abstract:

Chrysanthemum morifolium 'Chuju' is a famous medicinal species in traditional Chinese medicine. 'Chuju' contains various active ingredients that can be used to treat multiple diseases without toxic side effects. In the present study, we combined metabolomics and network pharmacology to further explore the active ingredients in 'Chuju', as well as the target and pharmacological mechanisms of the active ingredients. A visualized network analysis model based on chemistry, pharmacokinetics, and pharmacology was constructed to predict the potential pharmacological mechanism of 'Chuju'. A total of 424 metabolites of 'Chuju' were identified, of which 21 ingredients had positive pharmacological effects after ADME screening. The network pharmacology revealed that acacetin and beta-sitosterol in 'Chuju' could act on GSK3B, MAPK14, ADRA1R, and NOS2 to regulate diabetes mellitus, Alzheimer's disease, breast cancer, and inflammatory diseases. These results indicated the potential pharmacological effects of the active components in 'Chuju'.

Key words: Chrysanthemum morifolium 'Chuju', Metabonomics, Network pharmacology, Anti-cancer, Anti-diabetes

Supporting:

赵维萍, 葛奇, 丁子俊, 潘雷枝, 谷子晴, 刘洋, 蔡华. 基于网络药理学和代谢组学分析滁菊中潜在活性成分及其药理作用机制[J]. 中国药学(英文版), 2022, 31(6): 412-428.

Weiping Zhao, Qi Ge, Zijun Ding, Leizhi Pan, Ziqing Gu, Yang Liu, Hua Cai. Network pharmacology and metabolomics-based detection of the potential pharmacological effects of the active components in Chrysanthemum morifolium 'Chuju'[J]. Journal of Chinese Pharmaceutical Sciences, 2022, 31(6): 412-428.

图/表 9

Figure 1. Fresh 'Chuju' flowers were collected from four sequentially developmental stages: budding stage (BD stage), bud breaking stage (BB stage), early blooming stage (EB stage), and full blooming stage (FB stage).

Table 1. Quality of 'Chuju'.

Figure 2. Determination of metabolites in 'Chuju' at four flowering stages. (A) PCA diagram of metabolites in positive ion mode; (B) PCA diagram of metabolites in negative ion mode; (C) Venn analysis of metabolites identified under the positive and negative ion models; (D) The quantity ratio of metabolites in various chemical classifications.

Table 2. Ingredient information of 'Chuju'.

Figure 3. The interactions plot of active ingredients of 'Chuju' with target proteins and diseases. (A) Component-protein target interaction network model diagram. The green triangle represents the active ingredient, and the purple circle represents the protein target. (B) Components-disease interaction network model diagram. The green triangle represents the active ingredient, and the purple circle represents the disease.

Figure 4. Target PPI network analysis.

Figure 5. GO enrichment analysis of the target proteins.

Figure 6. KEGG analysis of target proteins (A) and target-pathway interaction analysis (B). The pink ellipse represents the target proteins, and the green diamond represents the pathways.

Figure 7. Distribution of the target proteins of 'Chuju' on the predicted pathway.

参考文献 49

[1]	Ren, L.P.; Sun, J.; Chen, S.M.; Gao, J.J.; Dong, B.; Liu, Y.N.; Xia, X.L.; Wang, Y.J.; Liao, Y.; Teng, N.J.; Fang, W.M.; Guan, Z.Y.; Chen, F.D.; Jiang, J.F. A transcriptomic analysis of Chrysanthemum nankingense provides insights into the basis of low temperature tolerance. BMC Genom. 2014, 15, 844.
[2]	Cui, H.Y.; Bai, M.; Sun, Y.H.; Abdel-Samie, M.A.S.; Lin, L. Antibacterial activity and mechanism of Chuzhou chrysanthemum essential oil. J. Funct. Foods. 2018, 48, 159–166.
[3]	Tyagi, S.; Jung, J.A.; Kim, J.S.; Kwon, S.J.; Won, S.Y. The complete chloroplast genome of an economic plant, Chrysanthemum morifolium 'Baekma'. Mitochondrial DNA B. 2019, 4, 3133–3134.
[4]	Yang, L.; Aobulikasimu•Nuerbiye, Cheng, P.; Wang, J.H.; Li, H. Analysis of floral volatile components and antioxidant activity of different varieties of chrysanthemum morifolium. Molecules. 2017, 22, 1790.
[5]	Gui, M.; Du, J.; Guo, J.M.; Xiao, B.Q.; Yang, W.; Li, M.J. Aqueous extract of chrysanthemum morifolium enhances the antimelanogenic and antioxidative activities of the mixture of soy peptide and collagen peptide. J. Tradit. Complement. Med. 2014, 4, 171–176.
[6]	Tu, X.; Wang, H.B.; Huang, Q.; Cai, Y.; Deng, Y.P.; Yong, Z.; Hu, Q.; Feng, J.; Jordan, J.B.; Zhong, S. Screening study on the anti-angiogenic effects of traditional Chinese medicine-part II: Wild chrysanthemum. J. Cancer. 2021, 12, 124–133.
[7]	Kim, H.J.; Lee, Y.S. Identification of new dicaffeoylquinic acids fromChrysanthemum morifoliumand their antioxidant activities. Planta Med. 2005, 71, 871–876.
[8]	Fan, Y.C.; Li, Y.; Cai, H.X.; Li, J.; Miao, J.; Fu, D.X.; Su, K. Three-dimensional fluorescence characteristics of white chrysanthemum flowers. Spectrochim. Acta A. 2014, 130, 411–415.
[9]	Yue, J.; Zhu, C.; Zhou, Y.; Niu, X.; Miao, M.; Tang, X.; Chen, F.; Zhao, W.; Liu, Y. Transcriptome analysis of differentially expressed unigenes involved in flavonoid biosynthesis during flower development of Chrysanthemum morifolium 'Chuju'. Sci. Rep. 2018, 8, 13414
[10]	Feng, S.G.; He, R.F.; Lu, J.J.; Jiang, M.Y.; Shen, X.X.; Jiang, Y.; Wang, Z.A.; Wang, H.Z. Development of SSR markers and assessment of genetic diversity in medicinal chrysanthemum morifolium cultivars. Front. Genet. 2016, 7, 113.
[11]	Xie, Y.Y.; Yuan, D.; Yang, J.Y.; Wang, L.H.; Wu, C.F. Cytotoxic activity of flavonoids from the flowers of Chrysanthemum morifolium on human colon cancer Colon205 cells. J. Asian Nat. Prod. Res. 2009, 11, 771–778.
[12]	He, D.X.; Ru, X.C.; Wen, L.; Wen, Y.C.; Jiang, H.D.; Bruce, I.C.; Jin, J.; Ma, X.; Xia, Q. Total flavonoids of Flos Chrysanthemi protect arterial endothelial cells against oxidative stress. J. Ethnopharmacol. 2012, 139, 68–73.
[13]	Han, A.R.; Nam, B.; Kim, B.R.; Lee, K.C.; Song, B.S.; Kim, S.; Kim, J.B.; Jin, C. Phytochemical composition and antioxidant activities of two different color chrysanthemum flower teas. Molecules. 2019, 24, 329.
[14]	Zhou, Y.; Liu, Z.L.; Chen, Y.C.; Jin, L.H. Identification of the protective effects of traditional medicinal plants against SDS-induced Drosophila gut damage. Exp. Ther. Med. 2016, 12, 2671–2680.
[15]	Sun, H.; Zhang, A.H.; Wang, X.J. Potential role of metabolomic approaches for Chinese medicine syndromes and herbal medicine. Phytother. Res. 2012, 26, 1466–1471.
[16]	Wu, G.S.; Li, H.K.; Zhang, W.D. Metabolomics and its application in the treatment of coronary heart disease with traditional Chinese medicine. Chin. J. Nat. Med. 2019, 17, 321–330.
[17]	Zhang, A.H.; Sun, H.; Wang, P.; Han, Y.; Wang, X.J. Metabonomics for discovering biomarkers of hepatotoxicity and nephrotoxicity. Die Pharmazie. 2012, 67, 99–105.
[18]	Pan, L.L.; Li, Z.Z.; Wang, Y.F.; Zhang, B.Y.; Liu, G.R.; Liu, J.H. Network pharmacology and metabolomics study on the intervention of traditional Chinese medicine Huanglian Decoction in rats with type 2 diabetes mellitus. J. Ethnopharmacol. 2020, 258, 112842.
[19]	Chen, C.C.; Yin, Q.C.; Tian, J.S.; Gao, X.X.; Qin, X.M.; Du, G.H.; Zhou, Y.Z. Studies on the potential link between antidepressant effect of Xiaoyao San and its pharmacological activity of hepatoprotection based on multi-platform metabolomics. J. Ethnopharmacol. 2020, 249, 112432.
[20]	Zhou, S.Z.; Allard, P.M.; Wolfrum, C.; Ke, C.Q.; Tang, C.P.; Ye, Y.; Wolfender, J.L. Identification of chemotypes in bitter melon by metabolomics: a plant with potential benefit for management of diabetes in traditional Chinese medicine. Metabolomics. 2019, 15, 104.
[21]	Su, G.Y.; Wang, H.F.; Bai, J.; Chen, G.; Pei, Y.H. A metabonomics approach to drug toxicology in liver disease and its application in traditional Chinese medicine. Curr. Drug Metab. 2019, 20, 292–300.
[22]	Wu, D.; Prives, C. Relevance of the p53-MDM2 axis to aging. Cell Death Differ. 2018, 25, 169–179.
[23]	Ru, J.L.; Li, P.; Wang, J.N.; Zhou, W.; Li, B.H.; Huang, C.; Li, P.D.; Guo, Z.H.; Tao, W.Y.; Yang, Y.F.; Xu, X.; Li, Y.; Wang, Y.H.; Yang, L. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform. 2014, 6, 13.
[24]	Fang, J.S.; Wang, L.; Wu, T.; Yang, C.; Gao, L.; Cai, H.B.; Liu, J.H.; Fang, S.H.; Chen, Y.B.; Tan, W.; Wang, Q. Network pharmacology-based study on the mechanism of action for herbal medicines in Alzheimer treatment. J. Ethnopharmacol. 2017, 196, 281–292.
[25]	Tsaioun, K.; Blaauboer, B.J.; Hartung, T. Evidence-based absorption, distribution, metabolism, excretion (ADME) and its interplay with alternative toxicity methods. ALTEX. 2016, 33, 343–358.
[26]	Ghosh, D. Incorporating the empirical null hypothesis into the benjamini-hochberg procedure. Stat. Appl. Genet. Mol. Biol. 2012, 11, 1–21.
[27]	Subiabre, M.; Villalobos-Labra, R.; Silva, L.; Fuentes, G.; Toledo, F.; Sobrevia, L. Role of insulin, adenosine, and adipokine receptors in the foetoplacental vascular dysfunction in gestational diabetes mellitus. Biochim. Biophys. Acta BBA Mol. Basis Dis. 2020, 1866, 165370.
[28]	Belfiore, A.; Malaguarnera, R.; Vella, V.; Lawrence, M.C.; Sciacca, L.; Frasca, F.; Morrione, A.; Vigneri, R. Insulin receptor isoforms in physiology and disease: an updated view. Endocr. Rev. 2017, 38, 379–431.
[29]	Haeusler, R.A.; McGraw, T.E.; Accili, D. Biochemical and cellular properties of insulin receptor signalling. Nat. Rev. Mol. Cell Biol. 2018, 19, 31–44.
[30]	Barbosa, K.; Li, S.; Adams, P.D.; Deshpande, A.J. The role of TP53 in acute myeloid leukemia: challenges and opportunities. Genes Chromosom. Cancer. 2019, 58, 875–888.
[31]	Silwal-Pandit, L.; Langerød, A.; Børresen-Dale, A.L. TP53 Mutations in breast and ovarian cancer. Cold Spring Harb. Perspect. Med. 2017, 7, a026252.
[32]	Wu, W.; Jiao, C.X.; Li, H.; Ma, Y.; Jiao, L.L.; Liu, S.Y. LC-MS based metabolic and metabonomic studies of Panax ginseng. Phytochem. Anal. 2018, 29, 331–340.
[33]	Xiao, H.; Qin, X.Y.; Wan, J.P.; Li, R. Pharmacological targets and the biological mechanisms of formononetin for Alzheimer's disease: a network analysis. Med. Sci. Monit. 2019, 25, 4273–4277.
[34]	Liao, M.L.; Shang, H.H.; Li, Y.Z.; Li, T.; Wang, M.; Zheng, Y.N.; Hou, W.B.; Liu, C.X. An integrated approach to uncover quality marker underlying the effects of Alisma orientale on lipid metabolism, using chemical analysis and network pharmacology. Phytomedicine. 2018, 45, 93–104.
[35]	Ye, X.W.; Deng, Y.L.; Xia, L.T.; Ren, H.M.; Zhang, J.L. Uncovering the mechanism of the effects of Paeoniae Radix Alba on iron-deficiency anaemia through a network pharmacology-based strategy. BMC Complement. Med. Ther. 2020, 20, 130.
[36]	Ogunleye, A.J.; Olanrewaju, A.J.; Arowosegbe, M.; Omotuyi, O.I. Molecular docking based screening analysis of GSK3B. Bioinformation. 2019, 15, 201–208.
[37]	Wu, H.; Lu, X.X.; Wang, J.R.; Yang, T.Y.; Li, X.M.; He, X.S.; Li, Y.; Ye, W.L.; Wu, Y.; Gan, W.J.; Guo, P.D.; Li, J.M. TRAF₆ inhibits colorectal cancer metastasis through regulating selective autophagic CTNNB₁/β-catenin degradation and is targeted for GSK3B/GSK3β-mediated phosphorylation and degradation. Autophagy. 2019, 15, 1506–1522.
[38]	Zheng, T.; Yang, X.Y.; Wu, D.; Xing, S.S.; Bian, F.; Li, W.J.; Chi, J.Y.; Bai, X.L.; Wu, G.J.; Chen, X.Q.; Zhang, Y.H.; Jin, S. Salidroside ameliorates insulin resistance through activation of a mitochondria-associated AMPK/PI3K/Akt/GSK3β pathway. Br. J. Pharmacol. 2015, 172, 3284–3301.
[39]	Yoshino, Y.; Ishioka, C. Inhibition of glycogen synthase kinase-3 beta induces apoptosis and mitotic catastrophe by disrupting centrosome regulation in cancer cells. Sci. Reports. 2015, 5, 13249.
[40]	Zeng, J.; Liu, D.; Qiu, Z.X.; Huang, Y.; Chen, B.J.; Wang, L.; Xu, H.; Huang, N.; Liu, L.X.; Li, W.M. GSK3β overexpression indicates poor prognosis and its inhibition reduces cell proliferation and survival of non-small cell lung cancer cells. PLoS One. 2014, 9, e91231.
[41]	Lee, S.; Rauch, J.; Kolch, W. Targeting MAPK signaling in cancer: mechanisms of drug resistance and sensitivity. Int. J. Mol. Sci. 2020, 21, 1102.
[42]	Kim, E.K.; Choi, E.J. Pathological roles of MAPK signaling pathways in human diseases. Biochim. Biophys. Acta BBA Mol. Basis Dis. 2010, 1802, 396–405.
[43]	McCubrey, J.A.; LaHair, M.M.; Franklin, R.A. Reactive oxygen species-induced activation of the MAP kinase signaling pathways. Antioxid. Redox Signal. 2006, 8, 1775–1789.
[44]	Dhillon, A.S.; Hagan, S.; Rath, O.; Kolch, W. MAP kinase signalling pathways in cancer. Oncogene. 2007, 26, 3279–3290.
[45]	Marques, C.A.; Keil, U.; Bonert, A.; Steiner, B.; Haass, C.; Müller, W.E.; Eckert, A. Neurotoxic mechanisms caused by the Alzheimer's disease-linked Swedish amyloid precursor protein mutation: oxidative stress, caspases, and the jnk pathway. J. Biol. Chem. 2003, 278, 28294–28302.
[46]	Chiarini, A.; dal Pra, I.; Marconi, M.; Chakravarthy, B.; Whitfield, J.F.; Armato, U. Calcium-sensing receptor (CaSR) in human brain's pathophysiology: roles in late-onset Alzheimer's disease (LOAD). Curr. Pharm. Biotechnol. 2009, 10, 317–326.
[47]	Kheiri, G.; Dolatshahi, M.; Rahmani, F.; Rezaei, N. Role of p38/MAPKs in Alzheimer's disease: implications for amyloid beta toxicity targeted therapy. Rev. Neurosci. 2018, 30, 9–30.
[48]	Melone, M.A.B.; Dato, C.; Paladino, S.; Coppola, C.; Trebini, C.; Giordana, M.T.; Perrone, L. Verapamil inhibits Ser202/Thr205 phosphorylation of tau by blocking TXNIP/ROS/p38 MAPK pathway. Pharm. Res. 2018, 35, 44.
[49]	Fang, J.Y.; Richardson, B.C. The MAPK signalling pathways and colorectal cancer. Lancet Oncol. 2005, 6, 322–327.

基于网络药理学和代谢组学分析滁菊中潜在活性成分及其药理作用机制

Network pharmacology and metabolomics-based detection of the potential pharmacological effects of the active components in Chrysanthemum morifolium 'Chuju'

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 9

参考文献 49

相关文章 15

编辑推荐

Metrics

本文评价

[1]	吴梦瑶, 刘璐, 张鹏, 张乐乐, 龚云, 杨秀伟. 基于网络药理学和实验验证研究补血益母丸治疗产后腹痛的作用机制[J]. 中国药学(英文版), 2023, 32(9): 691-703.
[2]	尚平, 刘琳, 方毅. 基于网络药理学和分子对接探讨桂枝茯苓丸治疗子宫内膜异位症的作用机制[J]. 中国药学(英文版), 2023, 32(9): 704-719.
[3]	张格第, 刘庚鑫, 晏子友. 基于meta分析和网络药理学理冲汤(丸)治疗癌症的疗效评价及作用机制研究[J]. 中国药学(英文版), 2023, 32(9): 720-735.
[4]	王慧敏, 赵雨营, 徐晓艳, 谢胡敏, 姜美婷, 王洪达, 徐蓓, 李晓航, 王思淼, 陈博学, 杨飞飞, 杨文志. 基于顶空进样气相色谱-质谱联用和非靶标代谢组学分析人参、西洋参和三七经蒸制后挥发性成分的转化[J]. 中国药学(英文版), 2023, 32(8): 645-664.
[5]	武东燕, 王小丹, 柴金苗, 李钦青, 李悦, 毕梅, 桂婉威, 曹慧敏. 基于网络药理学及实验验证探究当归补血汤治疗糖尿病性视网膜病变的作用机制[J]. 中国药学(英文版), 2023, 32(7): 527-538.
[6]	闫焕, 王健, 付浩, 杨敏, 曲苗, 方志娥. 基于网络药理学探讨大柴胡汤治疗高脂血症的潜在作用靶点和机制[J]. 中国药学(英文版), 2023, 32(6): 446-459.
[7]	王孟亚, 张宽友, 陈馨, 付浩, 彭守春. 基于网络药理学方法探讨犀角地黄汤治疗系统性红斑狼疮的作用机制[J]. 中国药学(英文版), 2023, 32(5): 351-359.
[8]	沈广志, 崔新刚, 那志敏, 邹玉龙, 邹桂华. 利用网络药理学探究淫羊藿治疗性功能障碍的药理作用机制[J]. 中国药学(英文版), 2023, 32(5): 379-391.
[9]	敖民, 包明兰, 侯亚星, 月英, 李慧芳, 吴国华, 苏日嘎拉图. 基于网络药理学的蒙药肋柱花抗急性肝损伤作用机制研究[J]. 中国药学(英文版), 2023, 32(4): 268-282.
[10]	李雅静, 白雅雯, 杜宇, 严长宏, 麻春杰, 孙丽宁, 卜凤跃, 严昊阳. 玉屏风散治疗慢性肾小球肾炎的临床疗效评价及作用机制研究[J]. 中国药学(英文版), 2023, 32(12): 1006-1026.
[11]	孙志勇, 高淑丽, 张阳, 薛刚强, 苑子林, 王少男. 基于网络药理学和分子对接技术研究蒲公英治疗乳腺增生的潜在机制[J]. 中国药学(英文版), 2023, 32(11): 893-910.
[12]	张玉倩, 牛海英, 靳怡然. 基于网络药理学技术探讨长春花治疗癌症的作用机制[J]. 中国药学(英文版), 2023, 32(11): 911-922.
[13]	周代英, 陈靓, 吕志刚. 基于网络药理学和分子对接探讨灯盏细辛治疗年龄相关性黄斑变性的机制[J]. 中国药学(英文版), 2023, 32(11): 923-934.
[14]	魏东升, 刘孝生, 李路珍, 齐佳杰, 王雨轩, 张哲. 基于综合生物信息学和单细胞测序方法揭示红花-丹参治疗冠心病的生物学和免疫学机制[J]. 中国药学(英文版), 2023, 32(10): 796-812.
[15]	丁宁, 张涛, 罗吉, 刘皓辰, 邓宇, 何永恒. 基于网络药理学和分子对接探究白芍七物汤治疗结直肠癌的作用机制[J]. 中国药学(英文版), 2023, 32(1): 17-31.